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The Vegetation Health Index (VHI) illustrates the severity of drought based on the vegetation health and the influence of temperature on plant conditions. The VHI is a composite index and the elementary indicator used to compute the seasonal drought indicators in ASIS: Agricultural Stress Index (ASI), Drought Intensity and Mean Vegetation Health Index (Mean VHI). VHI combines both the Vegetation Condition Index (VCI) and the Temperature Condition Index (TCI). The TCI is calculated using a similar equation to the VCI, but relates the current temperature to the long-term maximum and minimum , as it is assumed that higher temperatures tend to cause a deterioration in vegetation conditions. A decrease in the VHI would, for example, indicate relatively poor vegetation conditions and warmer temperatures, signifying stressed vegetation conditions, and over a longer period would be indicative of drought. In ASIS, VHI is computed in two modality: dekadal and monthly. The dekadal/monthly VHI raster layer published in Hand in Hand Geospatial platform is further updated in the following 5 dekads (improve data precision, remove cloud pixel etc.). Flags of raster file: 251=missing, 252=cloud, 253=snow, 254=sea, 255=background More information, please visit FAO GIEWS Earth Observation website: https://www.fao.org/giews/earthobservation/index.jsp?lang=en Data license policy: Creative Commons Attribution- NonCommercial-ShareAlike 3.0 IGO (CC BY-NC- SA 3.0 IGO) Recommended citation: © FAO - Agricultural Stress Index System (ASIS), http://www.fao.org/giews/earthobservation/, [Date accessed]
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The Vegetation Condition Index (VCI) evaluates the current vegetation health in comparison to the historical trends. The VCI relates current dekadal Normalized Difference Vegetation Index (NDVI) to its long-term minimum and maximum, normalized by the historical range of NDVI values for the same dekad. The VCI was designed to separate the weather-related component of the NDVI from the ecological element. 𝑉𝐶𝐼𝑖=(𝑁𝐷𝑉𝐼𝑖−𝑁𝐷𝑉𝐼𝑚𝑖𝑛) / (𝑁𝐷𝑉𝐼𝑚𝑎𝑥−𝑁𝐷𝑉𝐼𝑚𝑖𝑛) Together with Temperature Condition Index (TCI), Vegetation Condition Index (VCI) is used to calculate Vegetation Health Index (VHI) using formula: VHI=0.5*VCI+0.5*TCI In ASIS, VCI is computed in two modality: dekadal and monthly. The dekadal/monthly VCI raster layer published in Hand in Hand Geospatial platform is further updated in the following 5 dekads (improve data precision, remove cloud pixel etc.). Flags of raster file: 251=missing, 252=cloud, 253=snow, 254=sea, 255=background More information, please visit FAO GIEWS Earth Observation website: https://www.fao.org/giews/earthobservation/index.jsp?lang=en Recommended citation: © FAO - Agricultural Stress Index System (ASIS), http://www.fao.org/giews/earthobservation/, [Date accessed]
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The Vegetation Health Index (VHI) illustrates the severity of drought based on the vegetation health and the influence of temperature on plant conditions. The VHI is a composite index and the elementary indicator used to compute the seasonal drought indicators in ASIS: Agricultural Stress Index (ASI), Drought Intensity and Mean Vegetation Health Index (Mean VHI). VHI combines both the Vegetation Condition Index (VCI) and the Temperature Condition Index (TCI). The TCI is calculated using a similar equation to the VCI, but relates the current temperature to the long-term maximum and minimum , as it is assumed that higher temperatures tend to cause a deterioration in vegetation conditions. A decrease in the VHI would, for example, indicate relatively poor vegetation conditions and warmer temperatures, signifying stressed vegetation conditions, and over a longer period would be indicative of drought. In ASIS, VHI is computed in two modality: dekadal and monthly. The dekadal/monthly VHI raster layer published in Hand in Hand Geospatial platform is further updated in the following 5 dekads (improve data precision, remove cloud pixel etc.). Flags of raster file: 251=missing, 252=cloud, 253=snow, 254=sea, 255=background More information, please visit FAO GIEWS Earth Observation website: https://www.fao.org/giews/earthobservation/index.jsp?lang=en Recommended citation: © FAO - Agricultural Stress Index System (ASIS), http://www.fao.org/giews/earthobservation/, [Date accessed]
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Cropping intensity, defined as the number of cropping cycle(s) per year, is an important indicator to measure arable land use intensity. Tracking the change in cropping intensity can help assess the past development of the food production system and inform future agro-policies. All available images of top-of-atmosphere (TOA) reflectance from Landsat-7 ETM+, Landsat-8 OLI, Sentinel-2 MSI and MODIS during 2016–2018 were used for cropping intensity mapping via the GEE platform. To overcome the multi-sensor mismatch issue, an inter-calibration approach was adopted, which converted Sentinel-2 MSI and Landsat-8 OLI TOA reflectance data to the Landsat-7 ETM+ standard. Then the calibrated images were used to composite the 16-day TOA reflectance time series based on maximum composition method. To ensure data continuity, the MODIS NDVI product was used to fill temporal gaps with the following steps. First, the 250-m MODIS NDVI product was re-sized to 30-m using the bicubic algorithm. Then, the Whittaker algorithm was applied to the gap filled NDVI time series to smooth the NDVI time series. Two phenology metrics were introduced, mid-greenup and mid-greendown, which were derived as the day of year (DOY) at the transition points in the greenup and greendown periods when the smoothed NDVI time series passes 50% of the NDVI amplitude. An interval starting from mid-greenup and ending at mid-greendown is defined as a growing phenophase, and an interval moving from mid-greendown to mid-greenup a non-growing phenophase. Based on this phenophase-based approach, the global cropping intensity at 30m resolution (GCI30) was mapped. The results were validated based on a large number of ground-based samples obtained using GVG (GPS, Video and GIS) smart phone application and other crowd-sourcing dataset. The global cropping intensity dataset at 30m includes two layers. The first layer indicates the average cropping intensity during the three years from 2016 to 2018 with noData value or masked areas assigned to -1. The valid values for the first layer are 1, 2, and 3 representing single cropping, double cropping or triple cropping. The second layer keeps the original total number of crop cycles from 2016 to 2018 with noData value or masked areas assigned to -1. Continuous cropping or number of crop cycles larger than 3 per year are indicated with value of 127. Detailed documentation on the methodology of GCI30 can be found at the following two published papers: https://www.sciencedirect.com/science/article/abs/pii/S0034425720304685 https://essd.copernicus.org/articles/13/4799/2021/
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Seasonal maximum vegetation condition index (VCIx) is a remote sensing- based indicator introduced by CropWatch in 2014 for crop growth condition monitoring. VCIx adopts the general concept of Vegetation Condition Index (VCI) but stretches the length of temporal observation window from a short time slot, fixed by satellite sensor, to a period that can reflect various crop growth stages (crop phenology). In this way, it reduces the uncertainty of remote sensing index-based crop condition monitoring caused by inter-annual shifts (delay or advance) of crop phenology over different years. In CropWatch, VCIx is presented as a raster map at global extent with 1 Km resolution, updated every three months. Pixel values usually fall between 0 and 1. Based on the VCIx values, crop growth condition can be categorized into four levels: Level 1: VCIx＜0.5, indicating poor crop growth condition which is below the average of the previous 5 years (5YA) and 0 means as bad as the worst recent year; Level 2: 0.5≤VCIx＜0.8, indicating slight above 5YA situation; Level 3: 0.8≤VCIx≤1.0, indicating that crop condition is better than the 5YA but below the optimal condition during the previous five years, 1 means as good as the best recent year. Level 4: VCIx＞1.0, indicating a new record level of crop growth condition which exceeds the optimal condition of the previous 5 years. VCIx is calculated based on NDVI time series (MODIS). Peak NDVI during the monitoring period is compared with the historic (previous five years) minimum NDVI during the same period and normalized by the historical range of NDVI values for the same period. As NDVI values may be distorted by cloud or non-vegetation pixels, an empirical minimum vegetation NDVI value (0.15) is introduced in VCIx computation. In case the minimum NDVI of the monitoring period is lower than the empirical value (0.15), the empirical value (0.15) is used in the computation. Considering the genetic development and improvement of crops seeds, crops at monitoring year are hardly comparable with the same ones cultivated ten years ago. CropWatch uses previous five years, instead of a longer period, as the reference period when deriving the historic agronomic indicators. Detailed documentation on VCIx can be found at: http://cprs.patentstar.com.cn/Search/Detail?ANE=4CAA9DHB9DFABDIA9ICC9IGFAIIA9FFDCICA5CAA9ICC9DEB
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Cropped arable land fraction (CALF) was introduced to demonstrate the proportion of cropped arable land to the total arable land over a certain geographic area (Major Production Zones (MPZs), countries or sub-national units). Monitoring the dynamic changes in arable land utilization, specifically the dynamic identification of cropped and uncropped arable land, is important. CALF can reflect the rotation pattern of different crops and the change of cultivated land planting intensity, especially for early warning of crop planting area. On the basis of an analysis of profiles of time series NDVI, Savitzky-Golay filters are used to smooth the noise in NDVI curves, and Lagrange polynomials are employed to extract the extreme points for the smoothed NDVI curves. A threshold method associated with NDVI curve analysis is used to identify dynamic changes in the distribution of cropped and uncropped arable land. CALF over those regions was then calculated based on cropped and uncropped map and zonal statistical analysis. In CropWatch, CALF is presented as a statistical value updated every three months from raster map at global extent with 1 Km resolution for each spatial unit derived. The statistical value reflects the overall planting ratio. The Global raster maps show an area as cropped if at least one of the remote sensing observations during the monitoring period is categorized as "cropped". Uncropped means that no crops were detected over the whole reporting period. Based on the number of pixels for marked as "cropped" or "uncropped" within a certain spatial unit, CALF value is derived by the proportion of cropped pixels to the total arable land pixels (or cropped + uncropped pixels). CALF values are compared to the average value for the previous five years, with departures expressed in percentage. CALF is used as an early warning indicator for the planted area at the period of one month after emergence. Considering the genetic development and improvement of crops seeds, crops at monitoring year are hardly comparable with the same ones cultivated ten years ago. CropWatch uses previous five years, instead of a longer period, as the reference period when deriving the historic agronomic indicators. Detailed documentation on CALF can be found at: http://www.cropwatch.com.cn/htm/en/files/201682105626480.pdf
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Phenology is defined as the study of the timing of recurring biological cycles and their connection to the climate. Changes in the vegetation phenology, including the start of season, the length of the season as well as the end of season impacts the ecosystem functioning such as carbon storage, water holding capacity and agricultural productivity. Crop/pasture phenology maps depict the progress of the seasons. It is based on the long-term average of vegetation phenology for each pixel (In ASIS, Cropland/Grassland masks are applied. Pixel with at least 5% covered by the class are defined as a cropland/grassland pixel). This simplification implies that the crop/pasture phenology is static and therefore the growing seasons progress at a constant rate each year. The progress of growing seasons are described by three major phases: Start of Season (SOS), Maximum of Season (MOS) and End of Season (EOS). Start of Season (SOS) indicates the early stage of crop/grass emergence, defined as the date when the rising NDVI-curve cuts the threshold NDVIs: NDVIs=NDVImins + Ts.(NDVImax – NDVImins) NDVImax is the NDVI at the maximum of the cycle, NDVImins is the minimum before this maximum and threshold Ts is fixed to 0.25 for all land cover types. SOS is searched leftwards from NDVImax to NDVImins. Maximum of Season (MOS) indicates when crop/grass foliage is fully developed, defined as the date when the NDVI is at its maximum value. End of Season (EOS) indicates when crop/grass has reached physiological maturity, defined as the date when the descending NDVI-curve crosses NDVIe, This date does not necessarily correspond to the harvest period. NDVIe=NDVImine + Te.(NDVImax – NDVimine) NDVImax is the NDVI at the maximum of the cycle, NDVImine is the minimum after this maximum and threshold Te is set to 0.75 for cropland and to 0.25 for all other land. EOS is searched rightwards from NDVImax to NDVImine. Map legend label and pixel value mapping (half open intervals): <October Y-1: (-36)-(-6); November - December Y-1: (-6)-0; January - February: 0-6; March - April: 6-12; May - June: 12-18; July - August: 18-24; September - October: 24-30; November - December: 30-36; January - February Y+1: 36-42; >March Y+1: 42-72: Map flags: no seasons/no season 2: 251; no cropland/no grassland: 254 Global ASIS covers two crop/pasture seasons. Some countries have three or four crop seasons within a crop year. For these countries, Global ASIS cannot properly capture the crop phenology between the first and the last season (e.g. for a country has four crop seasons, the crop phenology of the 2nd and 3rd season). More information, please visit FAO GIEWS Earth Observation website: https://www.fao.org/giews/earthobservation/index.jsp?lang=en Recommended citation: © FAO - Agricultural Stress Index System (ASIS), http://www.fao.org/giews/earthobservation/, [Date accessed]
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The Vegetation Condition Index (VCI) evaluates the current vegetation health in comparison to the historical trends. The VCI relates current dekadal Normalized Difference Vegetation Index (NDVI) to its long-term minimum and maximum, normalized by the historical range of NDVI values for the same dekad. The VCI was designed to separate the weather-related component of the NDVI from the ecological element. 𝑉𝐶𝐼𝑖=(𝑁𝐷𝑉𝐼𝑖−𝑁𝐷𝑉𝐼𝑚𝑖𝑛) / (𝑁𝐷𝑉𝐼𝑚𝑎𝑥−𝑁𝐷𝑉𝐼𝑚𝑖𝑛) Together with Temperature Condition Index (TCI), Vegetation Condition Index (VCI) is used to calculate Vegetation Health Index (VHI) using formula: VHI=0.5*VCI+0.5*TCI In ASIS, VCI is computed in two modality: dekadal and monthly. The dekadal/monthly VCI raster layer published in Hand in Hand Geospatial platform is further updated in the following 5 dekads (improve data precision, remove cloud pixel etc.). Flags of raster file: 251=missing, 252=cloud, 253=snow, 254=sea, 255=background More information, please visit FAO GIEWS Earth Observation website: https://www.fao.org/giews/earthobservation/index.jsp?lang=en Data license policy: Creative Commons Attribution- NonCommercial-ShareAlike 3.0 IGO (CC BY-NC- SA 3.0 IGO) Recommended citation: © FAO - Agricultural Stress Index System (ASIS), http://www.fao.org/giews/earthobservation/, [Date accessed]
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Complementary to Agricultural Stress Index (ASI) which detects the severe and extreme drought, Drought Intensity is another quick-look indicator in ASIS that facilitates the early understanding of the intensity of the drought. It is a new indicator introduced in ASIS 2 (2018). Agricultural droughts are classified by their intensity and are categorized into four classes: Extreme, Severe, Moderate or Mild. The intensity of drought in ASIS is calculated through the weighted Mean Vegetation Health Index, indicating that the poorer the vegetation health the more severe the drought. Drought Intensity dekadal product is processed based on the conditions from the start of the season up to the current dekad. If differs from Drought Intensity Annual product which describes the drought conditions over the entire crop season. Important note: Map legend value= Pixel physical value * multiplier (100), except 251: off season, 252: no data, 253: no season and 254: no crop land, 255: water More information, please visit FAO GIEWS Earth Observation website: https://www.fao.org/giews/earthobservation/index.jsp?lang=en Data license policy: Creative Commons Attribution- NonCommercial-ShareAlike 3.0 IGO (CC BY-NC- SA 3.0 IGO) Recommended citation: © FAO - Agricultural Stress Index System (ASIS), http://www.fao.org/giews/earthobservation/, [Date accessed]
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Agricultural Stress Index (ASI) - Near Real Time is a quick-look indicator that facilitates the early identification of cropped land with a high likelihood of water stress (drought). It depicts the percentage of arable land, within an administrative area, that has been affected by drought conditions from the start of the season up to the current dekad. It differs from ASI Annual product which describes the drought conditions over the entire crop season. The Index is based on the integration of the Vegetation Health Index (VHI) in two dimensions that are critical in the assessment of a drought event in agriculture: temporal and spatial. The first step of the ASI calculation is a temporal averaging of the VHI, assessing the intensity and duration of dry periods occurring during the crop cycle at the pixel level; this calculation includes the use of crop coefficients, which introduces sensitivity of a crop to water stress during each phenological phase. The second step determines the spatial extent of drought events by calculating the percentage of pixels in arable areas with a VHI value below 35 percent (this value was identified as a critical threshold in assessing the extent of drought in previous research by Kogan, 1995). Each administrative area is classified according to the percentage of the affected area to facilitate the quick interpretation of results.